cadence

Concepts

The vocabulary of dependency grammar, each term defined the way the code measures it, with an example run through the same parser the tool ships. Every arc and every number in the figures comes from that parser. The Method page then shows how the measurements become a score, and the page ends with three Howard Marks memos put through all of it.

Token

A token is the unit the parser counts. Most tokens are words. Punctuation marks are tokens too, and a contraction splits into two, so didn't is did and n't. Sentence length in the profile counts tokens that are neither punctuation nor whitespace: "The board asked for a correction." is six.

A repair pass runs before the count, because the parser was trained on news and splits things a memo writer keeps whole. ISO dates and hyphenated compounds stay single tokens after it.

Part of speech

Every token carries two labels. The coarse one is a class from the Universal Dependencies set, seventeen in all: NOUN, VERB, ADJ, ADV, PRON, DET, ADP (preposition), AUX (auxiliary), CCONJ and SCONJ (coordinating and subordinating conjunction), PART (particle), NUM, PROPN (proper noun), PUNCT and a few rarer ones. The fine one is a Penn Treebank tag, and it carries grammar the coarse label drops: NN against NNS is singular against plural, VBD against VBN is a past tense against a past participle, JJ against JJS is an adjective against its superlative.

The profile uses the coarse labels for the part of speech mix and for pronoun counting, and the fine tags for deciding whether a verb is finite.

Head, dependent, arc

A dependency parse draws one arc for every token except one. The arc runs from a head to a dependent, and its label names the relation. In "The board asked for a correction", board depends on asked as its subject, The depends on board as its determiner, for depends on asked as a prepositional modifier and correction depends on for as the object of the preposition. The token with no head is the root, usually the main verb. The whole thing is a tree: one root, and every token reachable from it by following arcs downward.

pobj det nsubj prep det ROOT The board asked for a correction. DT NN VBD IN DT NN DET NOUN VERB ADP DET NOUN
Arcs run from head to dependent, with the arrowhead at the dependent. Under each word: its fine tag, then its coarse class. A dotted line marks the root.

The labels that appear on this site:

label relation example
nsubj nominal subject the board asked
nsubjpass subject of a passive the number was repeated
dobj direct object wrote the correction
det determiner the board
amod adjectival modifier other decks
advmod adverbial modifier read it twice
aux, auxpass auxiliary, passive auxiliary had corrected, was repeated
neg negation did not match
prep, pobj preposition and its object asked for a correction
agent the by phrase of a passive repeated by the team
mark subordinator introducing a clause because, that, if
cc, conj conjunction and coordinated item went out, and nobody corrected it
advcl, ccomp, xcomp, relcl, acl, csubj, pcomp the subordinate clause types, below
compound, flat, appos parts of one name or noun phrase Ann Lee
punct punctuation

Finite verb and clause

A verb is finite when it carries tense or is a modal. In Penn tags that is VBZ (asks), VBD (asked), VBP (ask, with a plural subject) and MD (will, could, might). Base forms after an auxiliary (did not match), participles (corrected, asking) and infinitives (to leave) are not finite.

The tool counts clauses by counting finite verbs, because a clause is one assertion and a finite verb is what asserts. "The numbers did not match" has one finite verb, did; match is a base form riding on it. "Pricing will hold" has one, the modal will. Finite clauses per sentence is one of the six sentence level features, and a sentence with no finite verb at all is a fragment.

Coordination and subordination

Coordination joins two things of equal rank with and, but, or. The conjunction is labelled cc and the second item hangs from the first on a conj arc, at the same level. Subordination makes one clause part of another, and the parser has a label for each way that can happen:

label kind of clause example
advcl adverbial: reason, time, condition, result she read it because the numbers did not match
ccomp clausal complement with its own subject the view is that pricing will hold
xcomp open complement, sharing a subject wanted to leave
relcl relative clause on a noun the note which nobody had corrected
acl other clausal modifier of a noun the number quoted by the board
csubj a clause as subject what he said was wrong
pcomp a clause as object of a preposition by measuring first

The profile counts subordinate clauses per sentence as the number of tokens carrying one of those seven labels, and coordinated items per sentence as the number of conj arcs, so "A, B and C" counts two.

Coordinated 10 tokens · 2 finite · 0 subordinate · 1 coordinate · depth 3 conj cc nsubj advmod aux nsubj dobj det neg ROOT She read it twice, and the numbers did not match. PRP VBD PRP RB CC DT NNS VBD RB VB Subordinated 10 tokens · 2 finite · 1 subordinate · 0 coordinate · depth 4 advcl mark nsubj advmod aux nsubj dobj det neg ROOT She read it twice because the numbers did not match. PRP VBD PRP RB IN DT NNS VBD RB VB
The same two clauses joined two ways. With and, the second verb hangs from the first on a dashed conj arc and the sentence measures depth 3. With because, it hangs on a solid advcl arc one level lower, depth 4. Both have two finite verbs, marked in the tag row: read and did.

The two read differently because of what the reader has to hold. A coordinated clause closes before the next opens. A subordinate clause stays open, and the sentence is unresolved until it closes. Austen's sentences in the sample corpus carry 2.6 subordinate clauses each; the draft that ships with the demo carries 1.8, and only a third as much coordination.

Embedding depth

Depth is the longest chain of arcs from the root down to a leaf, counting only the arcs that nest. The root alone is depth 1. Arcs labelled conj, cc, compound, flat, appos, punct and dep cost nothing, because they join siblings or parts of a single name, and a reader holds nothing open while crossing them. Without that rule, "with Ann Lee and Bob Ray" would score three levels of nesting for four names.

Depth 5 deepest chain: said › number › was › board › the relcl mark dobj nsubj nsubj det det acl acomp ROOT He said the number that the board quoted was wrong. Depth 3 deepest chain: met › with › Lee › Ann · dashed arcs cost nothing conj pobj nsubj prep compound cc compound ROOT She met with Ann Lee and Bob Ray.
The highlighted arcs are the deepest chain. In the first sentence said governs number, which governs the relative clause verb was, which governs board, which governs the: depth 5. In the second, the names hang on dashed compound and conj arcs, so seven tokens after the verb produce a depth of 3.

The analyzer reports a finding when a sentence passes depth 8. The profile only records the distribution. The number is dependency depth, measured head to leaf, and a phrase structure tree of the same sentence would give a different one.

Tokens before the main verb

The count of tokens to the left of the root, punctuation excluded. A sentence that opens with a long subject, a fronted clause or a stack of modifiers makes the reader wait for the verb; rhetoric calls that shape periodic. A sentence whose verb arrives in the second or third word and trails its qualifications behind is loose. Both are legitimate, and writers lean one way.

6 tokens before the main verb The note, which nobody had corrected, went out unchanged. 2 tokens before the main verb The note went out unchanged, and nobody had corrected it.
Shaded cells sit before the root verb. The relative clause in the first version holds went back to the seventh token; the second version reaches it at the third and puts the same clause after.

Passive voice

In a passive clause the grammatical subject is the thing acted on, and the verb takes a form of be plus a past participle. The parser labels the subject nsubjpass and the auxiliary auxpass. When the writer names the actor, it arrives in a by phrase labelled agent. Otherwise the clause is an agentless passive, and the actor is absent from the sentence rather than merely placed late.

Active 0 passive · 0 agentless · 2 tokens before the verb dobj det nsubj det ROOT The team repeated the number. DT NN VBD DT NN Passive, agent named 1 passive · 0 agentless · 3 tokens before the verb nsubjpass pobj det auxpass agent det ROOT The number was repeated by the team. DT NN VBD VBN IN DT NN Passive, agent suppressed 1 passive · 1 agentless · 3 tokens before the verb pobj nsubjpass nummod det auxpass prep amod ROOT The number was repeated in two other decks. DT NN VBD VBN IN CD JJ NNS
Highlighted arcs are the passive markers. The middle sentence keeps the actor in an agent arc; the last one has no agent arc, and nobody in it repeated anything.

The profile records two rates, passives per finite verb and agentless passives per finite verb, and rates neither. The analyzer, which is looking for defects rather than describing a style, flags every agentless passive as a suppressed agent, and also catches the reduced form with no auxiliary, as in "the number quoted to the board".

Fragments and punctuation predication

A fragment is a sentence with no finite verb. It asserts nothing, so the parser roots it on whatever is left: a noun, an adjective, an adverb.

Punctuation predication is a particular way of producing one. A colon, semicolon or dash stands where a verb would go: "Pricing: unchanged since March." The colon is doing the work of is. Punctuation cannot head a clause, so the parser roots on the noun and hangs the rest off it as a modifier.

Colon carrying the claim no finite verb on either side of the colon · root is a noun amod prep pobj ROOT Pricing: unchanged since March. NN JJ IN NNP Fragment no finite verb at all · root is an adverb nummod npadvmod ROOT Three days late. CD NNS RB
No tag in either row belongs to the finite set. The colon sentence roots on Pricing; the fragment roots on the adverb late.

The profile counts colon and dash predications per sentence as the number of those punctuation tokens, and the specification marks the result as measured but not a target, because the writing rules the generator follows ban the construction. The fragment rate is a target: some writers use fragments on purpose, and Crane's prose in the benchmark is one example.

Pronouns

Pronouns per hundred tokens counts every token the parser classes as PRON: the personal ones, the relative which and that when they stand for a noun, and quantifier pronouns like nobody. Narrative prose that follows people runs high; memo prose that names things runs low. The sample corpus runs at 12.6 per hundred tokens and the draft at 4.2.

Nominalisation

A nominalisation turns a claim into a noun, so the writer can handle it without asserting it: "the view is that pricing will hold" against "we think pricing will hold". The analyzer keeps a list of container nouns (view, read, sense, concern, thesis, assessment and others) and flags one when it heads a subject or a predicate and nobody owns it. With an owner, "her view", it passes. The analyzer reports it as a finding; the profile does not measure it.

Function words and the part of speech mix

Words divide into open classes, which admit new members every year, and closed classes, which do not: determiners, prepositions, pronouns, conjunctions, auxiliaries and particles. The closed class words are the function words. They carry no topic, so their frequencies survive a change of subject, and most authorship attribution work rests on them, beginning with Mosteller and Wallace, who separated Hamilton from Madison in the Federalist Papers in 1964 on words like upon and whilst.

The profile keeps the twenty five most frequent function words per hundred tokens. For the sample corpus and the demo draft, the top of each list:

sample corpus per 100 draft per 100
of 4.55 to 3.16
to 3.74 a 3.16
in 3.21 for 3.16
was 3.21 the 3.16
her 3.21 of 3.16
a 2.94 in 2.11
the 2.94 that 2.11
and 2.67 these 2.11

Her in fifth place is a fact about a novel with a female protagonist. These in eighth place is a fact about a draft that keeps gesturing at things it has already said.

The part of speech mix is the share of tokens in each coarse class, kept for the fourteen largest classes. It records whether a writer builds sentences out of nouns and prepositions or out of verbs and adverbs.

Distributions

The profile measures each of the six sentence level features once per sentence and keeps the resulting distribution: mean, standard deviation and the tenth, fiftieth and ninetieth percentiles. The specification the generator receives quotes the range from the tenth to the ninetieth percentile and the standard deviation, and tells it to land inside the range rather than on the mean.

sample corpus 1 sentence of 7 tokens 2 sentences of 8 tokens 2 sentences of 8 tokens 1 sentence of 12 tokens 1 sentence of 17 tokens 1 sentence of 23 tokens 1 sentence of 30 tokens 3 sentences of 31 tokens 3 sentences of 31 tokens 3 sentences of 31 tokens 1 sentence of 33 tokens 1 sentence of 41 tokens 1 sentence of 48 tokens 1 sentence of 54 tokens p10 8 p50 30 p90 48 draft 1 sentence of 12 tokens 2 sentences of 13 tokens 2 sentences of 13 tokens 1 sentence of 17 tokens 1 sentence of 18 tokens 1 sentence of 22 tokens p10 12 p50 13 p90 18 01020304050tokensmean± 1 sdpercentileone sentence
Sentence length, one dot per sentence, for the demo's sample corpus and its draft. Tick marks are the tenth, fiftieth and ninetieth percentiles; the triangle is the mean and the bar is one standard deviation each way. The corpus varies by 54 percent of its mean, the draft by 22.

The coefficient of variation is the standard deviation divided by the mean. Below a quarter, the checker reports uniform rhythm, which is the pattern readers most often identify as machine written. The draft above sits at 0.22.

Distance between two texts

Three kinds of feature, three distance rules, all normalised so that 0 is identical and 1 is a large gap.

A distribution feature compares means and spreads together: 0.65 times the difference in means, divided by the target mean, plus 0.35 times the difference in standard deviations, divided by the target standard deviation. The weight on spread is deliberate: a rewrite that matches the mean and flattens the variance is the usual failure.

A rate compares two numbers: the absolute difference divided by the target rate, with a floor of 0.5 on the divisor so that a rare feature cannot dominate.

A categorical feature, the part of speech mix or the function word list, is compared by total variation distance: half the sum, over every category, of the absolute difference in share. Two identical distributions score 0; two that share no category score 1.

Percent of tokens by part of speech, corpus / draft NOUN corpus NOUN 18.18% draft NOUN 25.26% 18.18 / 25.26 ADP corpus ADP 13.1% draft ADP 9.47% 13.1 / 9.47 PRON corpus PRON 12.57% draft PRON 4.21% 12.57 / 4.21 VERB corpus VERB 10.43% draft VERB 17.89% 10.43 / 17.89 ADJ corpus ADJ 10.43% draft ADJ 10.53% 10.43 / 10.53 AUX corpus AUX 8.56% draft AUX 3.16% 8.56 / 3.16 DET corpus DET 8.02% draft DET 8.42% 8.02 / 8.42 ADV corpus ADV 5.35% draft ADV 8.42% 5.35 / 8.42 CCONJ corpus CCONJ 4.28% draft CCONJ 2.11% 4.28 / 2.11sample corpusdrafttotal variation over all classes: 0.217
Part of speech shares for the sample corpus and the draft, percent of tokens. Half the summed gap across all classes is the total variation distance, 0.217. The same measure over the function word lists gives 0.481.

How the fifteen distances become the single 0 to 100 score is on the Method page.

The parser

The parser is spaCy's en_core_web_sm, a small statistical model trained on annotated news and web text. It assigns tags and arcs by prediction, which means it can be wrong, and it is wrong in repeatable ways on note register English: a date read as a number, a head noun read as a verb, an accusative pronoun read as a subject. The Method page lists the five repairs that run before measurement. The parse is the measurement instrument, and every figure on this page is what the instrument produced, corrections included.

Worked example: three Howard Marks memos

Howard Marks has written memos to Oaktree's clients since 1990. Three recent ones, Is It a Bubble?, What's Going On in Private Credit? and A Look Under the Hood, come to 744 sentences and 16,204 tokens of prose once headings, bullet lists and the legal footer are set aside. The memos are copyrighted, so the repository holds the numbers and two quoted sentences, and the script that produced them reads the text from wherever you saved it.

A sentence near the middle

Of the 744 sentences, this one from the private credit memo sits closest to the medians on length, depth, clause counts and verb position at once:

21 tokens · 2 finite · 2 subordinate · 1 coordinate · depth 6 prep nsubjpass advcl attr det auxpass ccomp det conj pobj amod advmod prep amod expl amod cc prep pobj det ROOT The preceding events were mostly treated as idiosyncratic, meaning there was no broad disillusionment or loss of confidence at the time. DT VBG NNS VBD RB VBN IN JJ VBG EX VBD DT JJ NN CC NN IN NN IN DT NN
“The preceding events were mostly treated as idiosyncratic, meaning there was no broad disillusionment or loss of confidence at the time.” 21 tokens, depth 6, two finite verbs, two subordinate clauses, one coordination, five tokens before the main verb, and one passive with no agent.

The shape recurs through all three memos: a main clause on a passive verb, a trailing participial clause that carries the point, and a coordinated pair inside it.

The deepest short sentence

Among sentences of 23 tokens or fewer, the deepest chain runs twelve arcs:

Depth 12 21 tokens · 3 finite · 2 subordinate · 0 coordinate · depth 12 relcl nsubj nsubj attr advmod dobj pobj relcl aux det prep pobj prep pobj advmod det prep det nsubj prep ROOT This should be a source of pause for people who heretofore fully comprehended the nature of the businesses they invested in.
“This should be a source of pause for people who heretofore fully comprehended the nature of the businesses they invested in.” A relative clause on people, a second relative clause on businesses inside it, and a stranded preposition at the end. Everything from source to in stays open until the last word.

The distribution

Marks 5 sentences of 4 tokens 11 sentences of 5 tokens 12 sentences of 6 tokens 23 sentences of 7 tokens 19 sentences of 8 tokens 26 sentences of 9 tokens 19 sentences of 10 tokens 12 sentences of 11 tokens 27 sentences of 12 tokens 37 sentences of 13 tokens 17 sentences of 14 tokens 23 sentences of 15 tokens 19 sentences of 16 tokens 26 sentences of 17 tokens 26 sentences of 18 tokens 32 sentences of 19 tokens 28 sentences of 20 tokens 26 sentences of 21 tokens 22 sentences of 22 tokens 26 sentences of 23 tokens 23 sentences of 24 tokens 22 sentences of 25 tokens 20 sentences of 26 tokens 26 sentences of 27 tokens 29 sentences of 28 tokens 21 sentences of 29 tokens 18 sentences of 30 tokens 14 sentences of 31 tokens 8 sentences of 32 tokens 17 sentences of 33 tokens 7 sentences of 34 tokens 6 sentences of 35 tokens 15 sentences of 36 tokens 11 sentences of 37 tokens 10 sentences of 38 tokens 5 sentences of 39 tokens 9 sentences of 40 tokens 1 sentence of 41 tokens 4 sentences of 42 tokens 7 sentences of 43 tokens 1 sentence of 44 tokens 4 sentences of 45 tokens 2 sentences of 46 tokens 3 sentences of 47 tokens 4 sentences of 48 tokens 2 sentences of 51 tokens 1 sentence of 52 tokens 1 sentence of 54 tokens 2 sentences of 56 tokens 2 sentences of 57 tokens 2 sentences of 58 tokens 1 sentence of 60 tokens 1 sentence of 66 tokens 1 sentence of 71 tokens p10 9 p50 21 p90 37 Austen sample 1 sentence of 7 tokens 2 sentences of 8 tokens 2 sentences of 8 tokens 1 sentence of 12 tokens 1 sentence of 17 tokens 1 sentence of 23 tokens 1 sentence of 30 tokens 3 sentences of 31 tokens 3 sentences of 31 tokens 3 sentences of 31 tokens 1 sentence of 33 tokens 1 sentence of 41 tokens 1 sentence of 48 tokens 1 sentence of 54 tokens p10 8 p50 30 p90 48 demo draft 1 sentence of 12 tokens 2 sentences of 13 tokens 2 sentences of 13 tokens 1 sentence of 17 tokens 1 sentence of 18 tokens 1 sentence of 22 tokens p10 12 p50 13 p90 18 010203040506070tokens
Sentence lengths for the three memos, the Austen sample and the demo draft on one axis. The Marks row has 744 sentences, so it shows a bar per length instead of a dot per sentence. Marks: mean 21.8 tokens, sd 11.1, tenth to ninetieth percentile 8 to 36. Austen: mean 26.7, sd 14.4. The draft: mean 15.8, sd 3.5.

Marks varies sentence length by 51 percent of its mean, Austen by 54, the draft by 22. On this axis an investor memo and an 1813 novel sit together and the machine draft sits apart.

Marks 37 tokens, depth 6 19 tokens, depth 10 26 tokens, depth 10 29 tokens, depth 10 20 tokens, depth 7 38 tokens, depth 9 37 tokens, depth 8 18 tokens, depth 7 15 tokens, depth 6 36 tokens, depth 10 25 tokens, depth 7 12 tokens, depth 4 20 tokens, depth 7 30 tokens, depth 8 27 tokens, depth 11 10 tokens, depth 4 28 tokens, depth 8 25 tokens, depth 6 30 tokens, depth 6 19 tokens, depth 5 20 tokens, depth 5 25 tokens, depth 7 33 tokens, depth 7 19 tokens, depth 5 24 sentences · mean 24.92 tokens · sd 7.77 · variation 31% of the mean Austen 23 tokens, depth 8 48 tokens, depth 9 31 tokens, depth 6 7 tokens, depth 4 12 tokens, depth 5 8 tokens, depth 3 17 tokens, depth 6 54 tokens, depth 7 31 tokens, depth 8 31 tokens, depth 13 33 tokens, depth 5 41 tokens, depth 9 8 tokens, depth 4 30 tokens, depth 6 14 sentences · mean 26.71 tokens · sd 14.44 · variation 54% of the mean draft 22 tokens, depth 7 13 tokens, depth 7 17 tokens, depth 5 12 tokens, depth 5 13 tokens, depth 8 18 tokens, depth 7 6 sentences · mean 15.83 tokens · sd 3.53 · variation 22% of the meanone bar per sentence, in reading order · width is length · darker is deeper
The first 24 sentences of Is It a Bubble?, the first 24 of the Austen sample and the six of the draft, in reading order. Width is sentence length and darker ink is deeper embedding.

The fifteen numbers

The six distributions as mean, standard deviation and the tenth to ninetieth percentile range, then five of the seven rates:

feature Marks Austen sample demo draft
sentence length, tokens 21.8sd 11.1, 8 to 36 26.7sd 14.4, 8 to 48 15.8sd 3.5, 12 to 18
embedding depth 6.4sd 2.4, 4 to 9 6.6sd 2.5, 4 to 9 6.5sd 1.1, 5 to 7
finite clauses per sentence 2.2sd 1.3, 1 to 4 2.5sd 1.1, 1 to 4 1.8sd 0.7, 1 to 2
subordinate clauses per sentence 1.9sd 1.7, 0 to 4 2.6sd 1.4, 1 to 4 1.8sd 1.1, 0 to 3
coordinated items per sentence 0.8sd 1.0, 0 to 2 1.3sd 1.3, 0 to 3 0.3sd 0.8, 0 to 0
tokens before the main verb 6.2sd 6.3, 1 to 15 8.2sd 7.1, 1 to 20 5.2sd 3.5, 1 to 7
fragments per sentence 0.027 0 0
passives per finite verb 0.103 0.171 0
agentless passives per finite verb 0.091 0.143 0
colon and dash predications per sentence 0.176 0.429 0
pronouns per 100 tokens 7.4 12.6 4.2

Marks writes shorter sentences than Austen at nearly the same depth, subordinates less, coordinates far less, uses half her pronouns and reaches the verb two tokens sooner. Against the draft the comparison runs the other way: the mean depth is the same, and the draft's depth never leaves 5 to 7 while his ranges 4 to 9. The draft has no passives, no fragments and no colons. He has some of each, in small and steady amounts.

The function words:

Marks per 100 Austen sample per 100 draft per 100
the 6.26 of 4.55 to 3.16
to 3.12 to 3.74 a 3.16
of 2.84 in 3.21 for 3.16
and 2.62 was 3.21 the 3.16
in 2.38 her 3.21 of 3.16
a 1.91 a 2.94 in 2.11
that 1.78 the 2.94 that 2.11
it 1.20 and 2.67 these 2.11

The at 6.3 per hundred tokens, twice Austen's rate, is a writer naming things: the market, the Fed, the bubble, the lender. Austen's her, was and he are a writer following people. Further down Marks' list sit I at 0.84 and but at 0.65, and neither appears in the draft's top twenty five.

Does the profile know him?

Each memo scored against a profile built from the other two, and against the Austen profile. Then the Austen passages the other way round, and the draft against both.

held out vs Marks profile vs Austen profile
Marks, A Look Under the Hood 83.2 74.7
Marks, Is It a Bubble? 86.0 69.0
Marks, What's Going On in Private Credit? 86.2 72.2
Austen passage, mean of four 54.7 56.7
the demo draft 68.7 55.9

A memo the profile never saw scores 83 to 86 against the other two memos and 69 to 75 against Austen, and the gap holds for all three. The Austen row is close because her sample is fourteen sentences in four short passages, which is the noise the Method page warns about. The draft lands nearer Marks than Austen, as a business memo in any voice should, and eighteen points short of his own unseen prose.

The specification

What the generator receives in profile mode, built from the three memos. No sentence of his is in it.

SYNTACTIC TARGET PROFILE (marks: 3 documents, 744 sentences, 16204 tokens)
LANGUAGE: English. The corpus is English prose and the output must be English.

Hit the RANGES, not the means. Varying within the range is required --
landing on the mean every sentence is the main way this goes wrong.

RHYTHM: vary sentence length by about 11.09 tokens (sd), matching the corpus at 51% of its mean.

 sentence length (tokens) 21.78 (typical range 8.0-36.0, sd 11.09)
 embedding depth 6.4 (typical range 4.0-9.0, sd 2.37)
 finite clauses per sentence 2.17 (typical range 1.0-4.0, sd 1.27)
 subordinate clauses/sentence 1.9 (typical range 0.0-4.0, sd 1.69)
 coordinated items/sentence 0.77 (typical range 0.0-2.0, sd 0.98)
 tokens before the main verb 6.17 (typical range 1.0-15.0, sd 6.3)

RATES
 sentences with no finite verb 0.027
 passives per finite verb 0.103
 agentless passives per finite verb 0.091
 colon/dash predications per sentence 0.176 <- MEASURED BUT NOT A TARGET: colon/dash predication is banned by remove-slop §1. Do not reproduce it.
 pronouns per 100 tokens 7.41
 subordinate clauses per sentence 1.901
 coordinated items per sentence 0.772

PART-OF-SPEECH MIX (% of tokens)
 NOUN 24.23%, VERB 12.88%, ADP 11.82%, DET 9.88%, ADJ 8.82%, PRON 7.41%, AUX 6.59%, ADV 4.55%, CCONJ 3.81%, PART 3.2%, PROPN 2.67%, SCONJ 2.57%, NUM 1.33%, SYM 0.12%

FUNCTION-WORD FREQUENCY (per 100 tokens) -- the strongest authorship signal
 the 6.26, to 3.12, of 2.84, and 2.62, in 2.38, a 1.91, that 1.78, it 1.2, for 1.13, is 1.09, be 0.96, i 0.84, but 0.65, on 0.64, as 0.54, they 0.54, this 0.53, will 0.52

A restyle against this specification is scored the way the benchmark scores one. The demo accepts any corpus, so the three memos and a draft of your own will reproduce every number on this page.